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Palosuo, T., Rötter, P., Lehtonen, H., Virkajärvi, P., & Salo, T. (2013). How to assess climate change impacts on farmers’ crop yields? (pp. 327–334).
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Nendel, C., Ewert, F., Rötter, R. P., Rosenzweig, C., Jones, J. W., Hatfield, J. L., et al. (2013). Addressing challenges and uncertainties for, the use of agro-ecosystem models to, assess climate change impact and food security across scales..
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Kersebaum, C., Kollas, C., Palosuo, T., Bindi, M., & Nendel, C. (2013). WP1 – Protocols, data formats and data classification scheme..
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Palosuo, T. (2013). Data format for model in- and output (Vol. 2).
Abstract: A common format for model input variables and model output variables has been defined to be distributed to modellers participating in the model inter-comparison and improvement. The aim of common formats is to support the communication between the modellers, those providing empirical data of the experiments and those analysing the simulation results. The input format facilitates the model application in a way that each cropping-system to be modelled will be defined in the same way. Data will be delivered in EXCEL sheets with sub-tables for each block of inputs. Tables are mostly organized in a way that allows export and sequential read-in by the models. The common output format enables effective processing of results estimating model performance indicators. No Label
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Eitzinger, J., Thaler, S., Schmid, E., Strauss, F., Ferrise, R., Moriondo, M., et al. (2013). Sensitivities of crop models to extreme weather conditions during flowering period demonstrated for maize and winter wheat in Austria. J. Agric. Sci., 151(6), 813–835.
Abstract: The objective of the present study was to compare the performance of seven different, widely applied crop models in predicting heat and drought stress effects. The study was part of a recent suite of model inter-comparisons initiated at European level and constitutes a component that has been lacking in the analysis of sources of uncertainties in crop models used to study the impacts of climate change. There was a specific focus on the sensitivity of models for winter wheat and maize to extreme weather conditions (heat and drought) during the short but critical period of 2 weeks after the start of flowering. Two locations in Austria, representing different agro-climatic zones and soil conditions, were included in the simulations over 2 years, 2003 and 2004, exhibiting contrasting weather conditions. In addition, soil management was modified at both sites by following either ploughing or minimum tillage. Since no comprehensive field experimental data sets were available, a relative comparison of simulated grain yields and soil moisture contents under defined weather scenarios with modified temperatures and precipitation was performed for a 2-week period after flowering. The results may help to reduce the uncertainty of simulated crop yields to extreme weather conditions through better understanding of the models’ behaviour. Although the crop models considered (DSSAT, EPIC, WOFOST, AQUACROP, FASSET, HERMES and CROPSYST) mostly showed similar trends in simulated grain yields for the different weather scenarios, it was obvious that heat and drought stress caused by changes in temperature and/or precipitation for a short period of 2 weeks resulted in different grain yields simulated by different models. The present study also revealed that the models responded differently to changes in soil tillage practices, which affected soil water storage capacity.
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